{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Han\Desktop\document\불평등 연구\주제12_부정선거\research and politics\data\4.syntax_robustness_1.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 3 Jul 2024, 00:03:44

{com}. encode country, gen(country_1)

. meologit subjective_class perceived_inequality income high_skilled low_skilled education age sex  marital religion urban political_orientation  i.year || country:, nolog
{res}
{txt}Mixed-effects ologit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    29,218
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        32

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        48
{col 63}{txt}avg{col 67}={res}{col 69}     913.1
{col 63}{txt}max{col 67}={res}{col 69}     1,981

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}12{txt}){col 67}={res}{col 70}  4794.43
{txt}Log likelihood = {res}-52230.292{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     subjective_class{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}perceived_inequality {c |}{col 23}{res}{space 2}-.0969153{col 35}{space 2}  .011421{col 46}{space 1}   -8.49{col 55}{space 3}0.000{col 63}{space 4}-.1193001{col 76}{space 3}-.0745305
{txt}{space 15}income {c |}{col 23}{res}{space 2} .2831684{col 35}{space 2} .0108149{col 46}{space 1}   26.18{col 55}{space 3}0.000{col 63}{space 4} .2619715{col 76}{space 3} .3043653
{txt}{space 9}high_skilled {c |}{col 23}{res}{space 2}  .437215{col 35}{space 2} .0274387{col 46}{space 1}   15.93{col 55}{space 3}0.000{col 63}{space 4}  .383436{col 76}{space 3} .4909939
{txt}{space 10}low_skilled {c |}{col 23}{res}{space 2}  .011768{col 35}{space 2} .0285393{col 46}{space 1}    0.41{col 55}{space 3}0.680{col 63}{space 4}-.0441681{col 76}{space 3} .0677041
{txt}{space 12}education {c |}{col 23}{res}{space 2} .2630739{col 35}{space 2}  .009629{col 46}{space 1}   27.32{col 55}{space 3}0.000{col 63}{space 4} .2442015{col 76}{space 3} .2819463
{txt}{space 18}age {c |}{col 23}{res}{space 2}-.0247701{col 35}{space 2} .0071507{col 46}{space 1}   -3.46{col 55}{space 3}0.001{col 63}{space 4}-.0387852{col 76}{space 3}-.0107551
{txt}{space 18}sex {c |}{col 23}{res}{space 2} .0080793{col 35}{space 2} .0218097{col 46}{space 1}    0.37{col 55}{space 3}0.711{col 63}{space 4} -.034667{col 76}{space 3} .0508256
{txt}{space 14}marital {c |}{col 23}{res}{space 2} .3272969{col 35}{space 2} .0222643{col 46}{space 1}   14.70{col 55}{space 3}0.000{col 63}{space 4} .2836598{col 76}{space 3} .3709341
{txt}{space 13}religion {c |}{col 23}{res}{space 2} .0395533{col 35}{space 2} .0254018{col 46}{space 1}    1.56{col 55}{space 3}0.119{col 63}{space 4}-.0102332{col 76}{space 3} .0893399
{txt}{space 16}urban {c |}{col 23}{res}{space 2} .1247998{col 35}{space 2} .0089646{col 46}{space 1}   13.92{col 55}{space 3}0.000{col 63}{space 4} .1072295{col 76}{space 3}   .14237
{txt}political_orientation {c |}{col 23}{res}{space 2}-.1383687{col 35}{space 2} .0127711{col 46}{space 1}  -10.83{col 55}{space 3}0.000{col 63}{space 4}-.1633995{col 76}{space 3}-.1133378
{txt}{space 21} {c |}
{space 17}year {c |}
{space 16}2019  {c |}{col 23}{res}{space 2} .1948285{col 35}{space 2}  .033294{col 46}{space 1}    5.85{col 55}{space 3}0.000{col 63}{space 4} .1295735{col 76}{space 3} .2600835
{txt}{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 16}/cut1 {c |}{col 23}{res}{space 2} -2.64376{col 35}{space 2} .1459832{col 46}{space 1}  -18.11{col 55}{space 3}0.000{col 63}{space 4}-2.929882{col 76}{space 3}-2.357639
{txt}{space 16}/cut2 {c |}{col 23}{res}{space 2}-1.633534{col 35}{space 2} .1423264{col 46}{space 1}  -11.48{col 55}{space 3}0.000{col 63}{space 4}-1.912489{col 76}{space 3}-1.354579
{txt}{space 16}/cut3 {c |}{col 23}{res}{space 2}-.5020644{col 35}{space 2} .1410252{col 46}{space 1}   -3.56{col 55}{space 3}0.000{col 63}{space 4}-.7784688{col 76}{space 3}  -.22566
{txt}{space 16}/cut4 {c |}{col 23}{res}{space 2} .4682328{col 35}{space 2} .1408404{col 46}{space 1}    3.32{col 55}{space 3}0.001{col 63}{space 4} .1921907{col 76}{space 3} .7442749
{txt}{space 16}/cut5 {c |}{col 23}{res}{space 2} 1.706396{col 35}{space 2} .1411382{col 46}{space 1}   12.09{col 55}{space 3}0.000{col 63}{space 4}  1.42977{col 76}{space 3} 1.983021
{txt}{space 16}/cut6 {c |}{col 23}{res}{space 2} 2.942972{col 35}{space 2} .1417499{col 46}{space 1}   20.76{col 55}{space 3}0.000{col 63}{space 4} 2.665147{col 76}{space 3} 3.220797
{txt}{space 16}/cut7 {c |}{col 23}{res}{space 2} 4.303825{col 35}{space 2}  .142852{col 46}{space 1}   30.13{col 55}{space 3}0.000{col 63}{space 4} 4.023841{col 76}{space 3}  4.58381
{txt}{space 16}/cut8 {c |}{col 23}{res}{space 2} 6.000649{col 35}{space 2} .1468618{col 46}{space 1}   40.86{col 55}{space 3}0.000{col 63}{space 4} 5.712805{col 76}{space 3} 6.288493
{txt}{space 16}/cut9 {c |}{col 23}{res}{space 2} 7.316272{col 35}{space 2} .1587725{col 46}{space 1}   46.08{col 55}{space 3}0.000{col 63}{space 4} 7.005084{col 76}{space 3} 7.627461
{txt}{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country              {col 23}{c |}
            var(_cons){c |}{col 23}{res}{space 2} .4303217{col 35}{space 2} .1092905{col 63}{space 4} .2615834{col 76}{space 3} .7079073
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. ologit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 2864.65{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. melogit high_class perceived_inequality income high_skilled low_skilled education age sex  marital religion urban political_orientation  i.year || country:, nolog
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    29,218
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        32

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        48
{col 63}{txt}avg{col 67}={res}{col 69}     913.1
{col 63}{txt}max{col 67}={res}{col 69}     1,981

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}12{txt}){col 67}={res}{col 70}  1193.08
{txt}Log likelihood = {res}-8937.1982{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           high_class{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}perceived_inequality {c |}{col 23}{res}{space 2}-.0979967{col 35}{space 2} .0202256{col 46}{space 1}   -4.85{col 55}{space 3}0.000{col 63}{space 4} -.137638{col 76}{space 3}-.0583553
{txt}{space 15}income {c |}{col 23}{res}{space 2} .2567246{col 35}{space 2} .0207921{col 46}{space 1}   12.35{col 55}{space 3}0.000{col 63}{space 4} .2159728{col 76}{space 3} .2974765
{txt}{space 9}high_skilled {c |}{col 23}{res}{space 2} .3422221{col 35}{space 2}  .052993{col 46}{space 1}    6.46{col 55}{space 3}0.000{col 63}{space 4} .2383577{col 76}{space 3} .4460866
{txt}{space 10}low_skilled {c |}{col 23}{res}{space 2} .0335848{col 35}{space 2} .0553196{col 46}{space 1}    0.61{col 55}{space 3}0.544{col 63}{space 4}-.0748396{col 76}{space 3} .1420092
{txt}{space 12}education {c |}{col 23}{res}{space 2} .2723105{col 35}{space 2} .0192758{col 46}{space 1}   14.13{col 55}{space 3}0.000{col 63}{space 4} .2345306{col 76}{space 3} .3100904
{txt}{space 18}age {c |}{col 23}{res}{space 2}  .047686{col 35}{space 2}  .013811{col 46}{space 1}    3.45{col 55}{space 3}0.001{col 63}{space 4}  .020617{col 76}{space 3} .0747549
{txt}{space 18}sex {c |}{col 23}{res}{space 2} -.071812{col 35}{space 2} .0411376{col 46}{space 1}   -1.75{col 55}{space 3}0.081{col 63}{space 4}-.1524403{col 76}{space 3} .0088162
{txt}{space 14}marital {c |}{col 23}{res}{space 2} .1947404{col 35}{space 2} .0426299{col 46}{space 1}    4.57{col 55}{space 3}0.000{col 63}{space 4} .1111872{col 76}{space 3} .2782935
{txt}{space 13}religion {c |}{col 23}{res}{space 2} -.015323{col 35}{space 2}  .047351{col 46}{space 1}   -0.32{col 55}{space 3}0.746{col 63}{space 4}-.1081293{col 76}{space 3} .0774833
{txt}{space 16}urban {c |}{col 23}{res}{space 2} .1260859{col 35}{space 2} .0171215{col 46}{space 1}    7.36{col 55}{space 3}0.000{col 63}{space 4} .0925283{col 76}{space 3} .1596435
{txt}political_orientation {c |}{col 23}{res}{space 2} -.168617{col 35}{space 2}  .023755{col 46}{space 1}   -7.10{col 55}{space 3}0.000{col 63}{space 4}-.2151759{col 76}{space 3}-.1220582
{txt}{space 21} {c |}
{space 17}year {c |}
{space 16}2019  {c |}{col 23}{res}{space 2} .2421007{col 35}{space 2} .0638262{col 46}{space 1}    3.79{col 55}{space 3}0.000{col 63}{space 4} .1170036{col 76}{space 3} .3671978
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2}-4.196539{col 35}{space 2} .1882994{col 46}{space 1}  -22.29{col 55}{space 3}0.000{col 63}{space 4}-4.565599{col 76}{space 3}-3.827479
{txt}{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country              {col 23}{c |}
            var(_cons){c |}{col 23}{res}{space 2} .3786165{col 35}{space 2} .1019788{col 63}{space 4} .2233222{col 76}{space 3} .6418996
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 719.16{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. melogit low_class perceived_inequality income high_skilled low_skilled education age sex  marital religion urban political_orientation  i.year || country:, nolog
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    29,218
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        32

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        48
{col 63}{txt}avg{col 67}={res}{col 69}     913.1
{col 63}{txt}max{col 67}={res}{col 69}     1,981

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}12{txt}){col 67}={res}{col 70}  1780.10
{txt}Log likelihood = {res}-9324.3119{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            low_class{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}perceived_inequality {c |}{col 23}{res}{space 2} .0578078{col 35}{space 2} .0215531{col 46}{space 1}    2.68{col 55}{space 3}0.007{col 63}{space 4} .0155645{col 76}{space 3}  .100051
{txt}{space 15}income {c |}{col 23}{res}{space 2}-.3682554{col 35}{space 2} .0200296{col 46}{space 1}  -18.39{col 55}{space 3}0.000{col 63}{space 4}-.4075127{col 76}{space 3}-.3289981
{txt}{space 9}high_skilled {c |}{col 23}{res}{space 2}-.5499222{col 35}{space 2} .0554979{col 46}{space 1}   -9.91{col 55}{space 3}0.000{col 63}{space 4} -.658696{col 76}{space 3}-.4411484
{txt}{space 10}low_skilled {c |}{col 23}{res}{space 2} .0829507{col 35}{space 2} .0532614{col 46}{space 1}    1.56{col 55}{space 3}0.119{col 63}{space 4}-.0214398{col 76}{space 3} .1873412
{txt}{space 12}education {c |}{col 23}{res}{space 2}-.2899861{col 35}{space 2} .0171051{col 46}{space 1}  -16.95{col 55}{space 3}0.000{col 63}{space 4}-.3235115{col 76}{space 3}-.2564608
{txt}{space 18}age {c |}{col 23}{res}{space 2} .0543664{col 35}{space 2} .0127469{col 46}{space 1}    4.27{col 55}{space 3}0.000{col 63}{space 4}  .029383{col 76}{space 3} .0793498
{txt}{space 18}sex {c |}{col 23}{res}{space 2}-.0988464{col 35}{space 2} .0396709{col 46}{space 1}   -2.49{col 55}{space 3}0.013{col 63}{space 4}   -.1766{col 76}{space 3}-.0210928
{txt}{space 14}marital {c |}{col 23}{res}{space 2} -.477941{col 35}{space 2} .0402064{col 46}{space 1}  -11.89{col 55}{space 3}0.000{col 63}{space 4}-.5567442{col 76}{space 3}-.3991379
{txt}{space 13}religion {c |}{col 23}{res}{space 2}-.1128215{col 35}{space 2} .0470122{col 46}{space 1}   -2.40{col 55}{space 3}0.016{col 63}{space 4}-.2049638{col 76}{space 3}-.0206792
{txt}{space 16}urban {c |}{col 23}{res}{space 2}-.1420968{col 35}{space 2} .0160022{col 46}{space 1}   -8.88{col 55}{space 3}0.000{col 63}{space 4}-.1734606{col 76}{space 3} -.110733
{txt}political_orientation {c |}{col 23}{res}{space 2} .1730861{col 35}{space 2}  .024969{col 46}{space 1}    6.93{col 55}{space 3}0.000{col 63}{space 4} .1241478{col 76}{space 3} .2220243
{txt}{space 21} {c |}
{space 17}year {c |}
{space 16}2019  {c |}{col 23}{res}{space 2}-.2818922{col 35}{space 2} .0613077{col 46}{space 1}   -4.60{col 55}{space 3}0.000{col 63}{space 4}-.4020531{col 76}{space 3}-.1617312
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2} -.055334{col 35}{space 2}  .187756{col 46}{space 1}   -0.29{col 55}{space 3}0.768{col 63}{space 4} -.423329{col 76}{space 3}  .312661
{txt}{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country              {col 23}{c |}
            var(_cons){c |}{col 23}{res}{space 2} .4633329{col 35}{space 2} .1213441{col 63}{space 4} .2773119{col 76}{space 3} .7741367
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 950.10{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Han\Desktop\document\불평등 연구\주제12_부정선거\research and politics\data\4.syntax_robustness_1.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 3 Jul 2024, 00:07:15
{txt}{.-}
{smcl}
{txt}{sf}{ul off}